Name
Abstract | ||
|---|---|---|
This letter proposes a generative network based on adversarial learning for synthesizing short-time audio streams and investigates the effectiveness of data augmentation for amphibian call sounds classification. Based on Fourier analysis, the generator is designed by a multi-layer perceptron composed of frequency basis learning layers and an output layer, and a discriminator is constructed by a convolutional neural network. Additionally, regularization on weights is introduced to train the networks with practical data that includes some disturbances. Synthetic audio streams are evaluated by quantitative comparison using inception score, and classification results are compared for real versus synthetic data. In conclusion, the proposed generative network is shown to produce realistic sounds and therefore useful for data augmentation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LSP.2020.2988199 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Generators, Gallium nitride, Training, Linear programming, Convolution, Streaming media, Generative adversarial networks, Generative model, adversarial networks, Wasserstein distance, audio stream generation | Journal | 27 |
ISSN | Citations | PageRank |
1070-9908 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| SangWook Park | 1 | 0 | 4.06 |
| Mounya Elhilali | 2 | 136 | 19.02 |
| David K. Han | 3 | 216 | 27.96 |
| Hanseok Ko | 4 | 421 | 80.24 |